Zero-shot learning (ZSL) is a technique used in machine learning where models can make predictions about classes they have never seen during training. This approach uses auxiliary information, such as attributes or semantic descriptions, to establish a relationship between known and unknown classes. By leveraging these descriptors, the model can generalize its understanding to recognize unseen classes based on their characteristics without needing direct examples.
For instance, imagine a model trained to recognize animals it has seen before, like dogs and cats. In a traditional supervised learning approach, if the model has never been shown a zebra, it won’t be able to identify one. However, in zero-shot learning, if the model knows attributes like "striped" and "four-legged," it can use that information to infer that a zebra, which shares these features, might belong to the “animal” class. The model essentially transfers its knowledge from known classes (dogs and cats) to the unseen class (zebras) based on the understanding of shared attributes or relationships, allowing it to classify new images accordingly.
To implement zero-shot learning effectively, developers often utilize techniques such as embedding spaces. In this approach, both the visual features of images and the semantic representations of classes are projected into a shared space. When a new class is introduced, its attributes are mapped into this space, enabling the model to associate existing knowledge with the new class. Consequently, developers can build systems capable of recognizing a wide variety of objects or categories without requiring extensive labeled datasets for every possible class, making it a powerful solution in scenarios with limited labeled data.